RDA

Defined in: ds/src/mva/estimators/RDA.js:57

Extends

Transformer

Constructors

Constructor

new RDA(params?): RDA

Defined in: ds/src/mva/estimators/RDA.js:58

Parameters

params?

Returns

RDA

Overrides

Transformer.constructor

Properties

_state

_state: object

Defined in: ds/src/core/estimators/estimator.js:27

Inherited from

Transformer._state

_warnings

_warnings: any[]

Defined in: ds/src/core/estimators/estimator.js:29

Inherited from

Transformer._warnings

fitted

fitted: boolean

Defined in: ds/src/core/estimators/estimator.js:25

Inherited from

Transformer.fitted

model

model: any

Defined in: ds/src/mva/estimators/RDA.js:62

params

params: object

Defined in: ds/src/mva/estimators/RDA.js:61

constrained

constrained: boolean = true

omit_missing

omit_missing: boolean = true

scale

scale: boolean = false

scaling

scaling: number = 2

Inherited from

Transformer.params

Methods

_prepareArgsForFit()

_prepareArgsForFit(args?): { columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Defined in: ds/src/core/estimators/estimator.js:367

Convenience helper: parse arguments passed to fit/predict/transform.

Supports declarative table-style inputs:

fit({ X, y, data, omit_missing })
fit({ data, columns, … })

Returns an object { X, y, prepared, rows } where X/y are numeric arrays if preparation was required, otherwise returns the original values.

Note: this helper only prepares numeric matrices/vectors using core table utilities; it does not perform encoding of categorical predictors.

Parameters

args?

any[] = []

Returns

{ columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Inherited from

Transformer._prepareArgsForFit

_repr_html_()

_repr_html_(): string

Defined in: ds/src/core/estimators/estimator.js:201

Observable/Jupyter HTML representation

Returns

string

HTML representation

Inherited from

Transformer._repr_html_

clearWarnings()

clearWarnings(): void

Defined in: ds/src/core/estimators/estimator.js:139

Clear all warnings

Returns

void

Inherited from

Transformer.clearWarnings

fit()

fit(Y, X?, opts?): RDA

Defined in: ds/src/mva/estimators/RDA.js:79

Fit the RDA model, constraining the response table Y by the predictor table X.

Accepts a positional numeric call (fit(Y, X[, opts])) or a declarative { data, response, predictors } object.

Parameters

Y

any

Response matrix (n samples × q responses), or a declarative { data, response, predictors } object

X?

number[][] = null

Predictor/explanatory matrix (n samples × p predictors), for the positional call form

opts?

Fitting options

constrained?

boolean

Whether to fit the constrained (RDA) rather than unconstrained ordination

omit_missing?

boolean

Whether to drop rows with missing values (declarative inputs)

scale?

boolean

Whether to scale columns to unit variance

scaling?

number

Ordination scaling convention

Returns

RDA

The fitted estimator (for chaining)

Overrides

Transformer.fit

fitTransform()

fitTransform(…args): void

Defined in: ds/src/core/estimators/estimator.js:683

Convenience: fit then transform Returns transformed data.

Parameters

args

…any[]

Returns

void

Inherited from

Transformer.fitTransform

getMemoryUsage()

getMemoryUsage(): string

Defined in: ds/src/core/estimators/estimator.js:97

Get memory usage in human-readable format

Returns

string

Memory usage string (e.g., “2.3 MB” or “145 KB”)

Inherited from

Transformer.getMemoryUsage

getParams()

getParams(): any

Defined in: ds/src/core/estimators/estimator.js:294

Get a shallow copy of parameters.

Returns

any

Inherited from

Transformer.getParams

getScores()

getScores(type?, scaled?): any

Defined in: ds/src/mva/estimators/RDA.js:204

Retrieve site (scores), response loadings, or predictor constraint scores.

Parameters

type?

scaled?

boolean = true

Returns

any

getState()

getState(): any

Defined in: ds/src/core/estimators/estimator.js:65

Get comprehensive model state

Returns

any

State information including fitted status, memory estimate, warnings

Inherited from

Transformer.getState

getWarnings()

getWarnings(): any[]

Defined in: ds/src/core/estimators/estimator.js:124

Get all warnings

Returns

any[]

Array of warning objects

Inherited from

Transformer.getWarnings

getWarningsByType()

getWarningsByType(type): any[]

Defined in: ds/src/core/estimators/estimator.js:148

Get warnings of a specific type

Parameters

type

string

Warning type

Returns

any[]

Filtered warnings

Inherited from

Transformer.getWarningsByType

hasWarnings()

hasWarnings(): boolean

Defined in: ds/src/core/estimators/estimator.js:132

Check if model has warnings

Returns

boolean

Inherited from

Transformer.hasWarnings

isFitted()

isFitted(): boolean

Defined in: ds/src/core/estimators/estimator.js:36

Check if model is fitted

Returns

boolean

Inherited from

Transformer.isFitted

predict()

predict(): void

Defined in: ds/src/core/estimators/estimator.js:424

Predict should be implemented by supervised estimators.

Returns

void

Inherited from

Transformer.predict

save()

save(): string

Defined in: ds/src/core/estimators/estimator.js:329

Save model to JSON string

Returns

string

JSON representation of the model

Inherited from

Transformer.save

setParams()

setParams(params?): RDA

Defined in: ds/src/core/estimators/estimator.js:285

Set parameters (mutates instance).

Parameters

params?

any = {}

Returns

RDA

Inherited from

Transformer.setParams

summary()

summary(): object

Defined in: ds/src/mva/estimators/RDA.js:173

Returns

object

constrained

constrained: any

constrainedVariance

constrainedVariance: any

eigenvalues

eigenvalues: any

predictors

predictors: any = p

responses

responses: any = q

samples

samples: any = n

scaling

scaling: any

varianceExplained

varianceExplained: any

toJSON()

toJSON(): object

Defined in: ds/src/mva/estimators/RDA.js:239

Serialize minimal model metadata. Subclasses may override to include learned parameters.

Returns

object

class

__class__: string = 'RDA'

fitted

fitted: boolean

model

model: any

params

params: any

Overrides

Transformer.toJSON

transform()

transform(Y, X?, opts?): any[]

Defined in: ds/src/mva/estimators/RDA.js:147

Transform new data into RDA canonical (site) scores using the fitted model.

Accepts positional numeric matrices (transform(Y, X)) or a declarative { data, response, predictors } object.

Parameters

Y

any

Response matrix (n samples × q responses), or a declarative { data, response, predictors } object

X?

number[][]

Predictor/explanatory matrix (n samples × p predictors), for the positional call form

opts?

Transform options

omit_missing?

boolean

Whether to drop rows with missing values (declarative inputs)

Returns

any[]

Canonical score objects, one per row (keyed rda1, rda2, …)

Overrides

Transformer.transform

fromJSON()

static fromJSON(obj?): RDA

Defined in: ds/src/mva/estimators/RDA.js:248

Basic deserialization. Subclasses should override if they need to restore learned arrays / matrices.

Parameters

obj?

Returns

RDA

Overrides

Transformer.fromJSON

load()

static load(jsonString): Estimator

Defined in: ds/src/core/estimators/estimator.js:346

Load model from JSON string

Parameters

jsonString

string

JSON representation

Returns

Estimator

Reconstructed estimator instance

Inherited from

Transformer.load